





"""simple docstring"""





import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {"vocab_file": "vocab.json"}

SCREAMING_SNAKE_CASE__   =  {
    "vocab_file": {
        "mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
    }
}

SCREAMING_SNAKE_CASE__   =  {"mgp-str": 27}

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE							      = PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE							      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





      def __init__(			self ,		lowercase ,		lowercase="[GO]" ,		lowercase="[GO]" ,		lowercase="[s]" ,		lowercase="[GO]" ,		**lowercase					)						->	Dict:
            super().__init__(
                unk_token=lowercase ,		bos_token=lowercase ,		eos_token=lowercase ,		pad_token=lowercase ,		**lowercase ,		)

            with open(lowercase ,		encoding="""utf-8"""					) as vocab_handle:
                  lowerCAmelCase    		=			json.load(lowercase					)
            lowerCAmelCase    		=			{v: k for k, v in self.vocab.items()}





      @property
      def        _snake_case     (			self					)						->	Any:
            return len(self.vocab					)





      def        _snake_case     (			self					)						->	List[Any]:
            return dict(self.vocab ,		**self.added_tokens_encoder					)





      def        _snake_case     (			self ,		lowercase					)						->	int:
            lowerCAmelCase    		=			[]
            for s in text:
                  char_tokens.extend(lowercase					)
            return char_tokens





      def        _snake_case     (			self ,		lowercase					)						->	List[str]:
            return self.vocab.get(lowercase ,		self.vocab.get(self.unk_token					)					)





      def        _snake_case     (			self ,		lowercase					)						->	Union[str, Any]:
            return self.decoder.get(lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase = None					)						->	Tuple[str]:
            if not os.path.isdir(lowercase					):
                  logger.error("""Vocabulary path ({}) should be a directory""".format(lowercase					)					)
                  return
            lowerCAmelCase    		=			os.path.join(
                lowercase ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

            with open(lowercase ,		"""w""" ,		encoding="""utf-8"""					) as f:
                  f.write(json.dumps(self.vocab ,		indent=2 ,		sort_keys=lowercase ,		ensure_ascii=lowercase					) + """\n"""					)

            return (vocab_file,)













"""simple docstring"""





import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 						lowercase   (   unittest.TestCase   ):





      @property
      def        _snake_case     (			self					)						->	List[str]:
            torch.manual_seed(0					)
            lowerCAmelCase    		=			UNetaDModel(
                block_out_channels=(32, 64) ,		layers_per_block=2 ,		sample_size=32 ,		in_channels=3 ,		out_channels=3 ,		down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") ,		up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") ,		)
            return model





      def        _snake_case     (			self					)						->	List[Any]:
            lowerCAmelCase    		=			self.dummy_uncond_unet
            lowerCAmelCase    		=			ScoreSdeVeScheduler()

            lowerCAmelCase    		=			ScoreSdeVePipeline(unet=lowercase ,		scheduler=lowercase					)
            sde_ve.to(lowercase					)
            sde_ve.set_progress_bar_config(disable=lowercase					)

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=2 ,		output_type="""numpy""" ,		generator=lowercase					).images

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=2 ,		output_type="""numpy""" ,		generator=lowercase ,		return_dict=lowercase					)[
                0
            ]

            lowerCAmelCase    		=			image[0, -3:, -3:, -1]
            lowerCAmelCase    		=			image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            lowerCAmelCase    		=			np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
            assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2







@slow
@require_torch
class 						lowercase   (   unittest.TestCase   ):





      def        _snake_case     (			self					)						->	Optional[int]:
            lowerCAmelCase    		=			"""google/ncsnpp-church-256"""
            lowerCAmelCase    		=			UNetaDModel.from_pretrained(lowercase					)

            lowerCAmelCase    		=			ScoreSdeVeScheduler.from_pretrained(lowercase					)

            lowerCAmelCase    		=			ScoreSdeVePipeline(unet=lowercase ,		scheduler=lowercase					)
            sde_ve.to(lowercase					)
            sde_ve.set_progress_bar_config(disable=lowercase					)

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=10 ,		output_type="""numpy""" ,		generator=lowercase					).images

            lowerCAmelCase    		=			image[0, -3:, -3:, -1]

            assert image.shape == (1, 256, 256, 3)

            lowerCAmelCase    		=			np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2






